Comparative Performance Analysis of Quantum Feature Maps for Quantum Kernel-based Machine Learning
Date
Authors
Jha, Ravi Kumar
Kasabov, Nikola
Bhattacharyya, Saugat
Coyle, Damien
Prasad, Girijesh
Supervisor
Item type
Journal Article
Degree name
Journal Title
Journal ISSN
Volume Title
Publisher
Nature Portfolio
Abstract
Quantum algorithms have become a popular research domain in recent times for discovering quantum-enhanced solutions in machine learning applications. Quantum kernels are one of the directions that establish such quantum-enhanced solutions to some extent. This work presents a detailed analysis of the quantum kernel approach leveraging feature maps and relevant hyperparameters to develop enhanced quantum kernels. The study includes a new high-order feature map and assesses five existing state-of-the-art feature maps for enhanced quantum kernel classifiers. Additionally, the significance of the rotational factor as a hyperparameter is highlighted for improving kernel performance. Also, it is analyzed whether different hyperparameter-tuned feature maps can lead to enhanced decision boundaries, demonstrating kernel expressivity. The analysis is undertaken on classification tasks using four different nonlinear datasets of distinct complexity. Comparative evaluations are also made with traditional machine learning models-Support Vector Machines (Linear and RBF), Naïve Bayes, Linear Discriminant Analysis, Decision Tree, Random Forest, Adaptive Boosting, and MLP. Overall, the study demonstrates that a well-tuned quantum feature map can enhance the generalization ability of quantum kernels, making them more effective for broader quantum-enhanced machine learning applications.Description
Keywords
Classification, Encoding function, Feature map, Machine learning, Quantum kernel, Classification, Encoding function, Feature map, Machine learning, Quantum kernel, 46 Information and Computing Sciences, 4611 Machine Learning, Networking and Information Technology R&D (NITRD), Machine Learning and Artificial Intelligence
Source
Scientific Reports, ISSN: 2045-2322 (Print); 2045-2322 (Online), Nature Portfolio, 16(1), 8142-. doi: 10.1038/s41598-026-39392-9
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